Social media is exerting an increasingly significant and sometimes unanticipated effect on individuals, business, even nations. It is increasingly important for organizations to be actively aware of any consequences that might arise from social media usage by its employees. Social media awareness can also be a useful tool in expeditiously sifting through a large number of recruitment candidates, reducing the total number of man-hours that is required to complete recruitment tasks.
Today a preponderance of people in the developed world have used social media in one form or another, creating social media profiles that allow them to share information with others. Many people have multiple accounts across different social media networks. The information a person shares can be very different from network to network, due to the different character and culture of each network.
For example, an individual may have a profile on Facebook™ and an employee profile in an organization's internal system. His Facebook™ profile indicates that he is interested in surfing and fencing, while his employee profile only contains information relevant to his employment and therefore does not mention his interest in surfing or fencing. A single profile contains a proper subset of all the data that could be in a profile for a person. Thus, the data one may have on a person from one profile may be incomplete. Any automated algorithms that process data by traversing the social media graph would benefit from being able to correctly recognize multiple profiles belonging to the same person, instead of incorrectly identifying them as separate individuals. For example, an attempt to count the number of social media fans for a particular brand would be much more accurate if the same person were not counted multiple times for each profile on LinkedIn™, Twitter™, and Facebook™.
The fragmentation of personal data across different social media networks presents a number of difficulties when attempting to determine if two different profiles belong to the same person. People can fill out different subsets of their personal information for each network, based on their perceived importance and relevance for each network. For example, the employment history might be fully described in a person's LinkedIn profile, but left blank in the Facebook profile, while his interest in surfing is mentioned in Facebook but not LinkedIn. People can also intentionally or unintentionally enter data inconsistencies across different networks. An individual might enter an inconsistent birthday to appear younger on Facebook, while desiring to appear older on LinkedIn. Older social media profiles that are no longer being actively updated may present different addresses or phone numbers than currently maintained profiles. A person might use a casual nickname on Facebook while desiring to appear more formal on LinkedIn. Beyond trusting the intentions of the social media user, conventional techniques for processing social media information typically lack a practical way to verify data's validity.